2 x Intel Xeon Platinum 8490H testing with a Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS) and ASPEED on Ubuntu 23.10 via the Phoronix Test Suite.
Compare your own system(s) to this result file with the
Phoronix Test Suite by running the command:
phoronix-test-suite benchmark 2310248-NE-SAPPHIRER04
sapphire rapids october
2 x Intel Xeon Platinum 8490H testing with a Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS) and ASPEED on Ubuntu 23.10 via the Phoronix Test Suite.
a:
Processor: 2 x Intel Xeon Platinum 8490H @ 3.50GHz (120 Cores / 240 Threads), Motherboard: Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS), Chipset: Intel Device 1bce, Memory: 1008GB, Disk: 3201GB Micron_7450_MTFDKCC3T2TFS, Graphics: ASPEED, Network: 2 x Intel X710 for 10GBASE-T
OS: Ubuntu 23.10, Kernel: 6.6.0-rc5-phx-patched (x86_64), Desktop: GNOME Shell 45.0, Display Server: X Server 1.21.1.7, Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1200
b:
Processor: 2 x Intel Xeon Platinum 8490H @ 3.50GHz (120 Cores / 240 Threads), Motherboard: Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS), Chipset: Intel Device 1bce, Memory: 1008GB, Disk: 3201GB Micron_7450_MTFDKCC3T2TFS, Graphics: ASPEED, Network: 2 x Intel X710 for 10GBASE-T
OS: Ubuntu 23.10, Kernel: 6.6.0-rc5-phx-patched (x86_64), Desktop: GNOME Shell 45.0, Display Server: X Server 1.21.1.7, Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1200
OpenVKL 2.0.0
Benchmark: vklBenchmarkCPU ISPC
Items / Sec > Higher Is Better
a . 2684 |=====================================================================
b . 2672 |=====================================================================
OpenVKL 2.0.0
Benchmark: vklBenchmarkCPU Scalar
Items / Sec > Higher Is Better
a . 1022 |=====================================================================
b . 1021 |=====================================================================
easyWave r34
Input: e2Asean Grid + BengkuluSept2007 Source - Time: 2400
Seconds < Lower Is Better
a . 125.74 |================================================================
b . 132.55 |===================================================================
oneDNN 3.3
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 1063.85 |=================================================================
b . 1081.47 |==================================================================
oneDNN 3.3
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 1099.08 |==================================================================
b . 1081.21 |=================================================================
oneDNN 3.3
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 1073.23 |================================================================
b . 1108.59 |==================================================================
oneDNN 3.3
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 831.25 |===================================================================
b . 828.75 |===================================================================
oneDNN 3.3
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 808.38 |===================================================================
b . 813.51 |===================================================================
oneDNN 3.3
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 774.63 |===============================================================
b . 823.92 |===================================================================
easyWave r34
Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200
Seconds < Lower Is Better
a . 52.60 |===================================================================
b . 52.99 |====================================================================
oneDNN 3.3
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 15.87 |====================================================================
b . 15.48 |==================================================================
oneDNN 3.3
Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 0.434631 |=================================================================
b . 0.418805 |===============================================================
oneDNN 3.3
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 0.414613 |=================================================================
b . 0.398996 |===============================================================
Embree 4.3
Binary: Pathtracer - Model: Asian Dragon Obj
Frames Per Second > Higher Is Better
a . 114.12 |===================================================================
b . 114.72 |===================================================================
Embree 4.3
Binary: Pathtracer ISPC - Model: Asian Dragon Obj
Frames Per Second > Higher Is Better
a . 131.43 |===================================================================
b . 131.90 |===================================================================
oneDNN 3.3
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 2.28356 |=====================================================
b . 2.82237 |==================================================================
oneDNN 3.3
Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 9.95622 |==================================================================
b . 9.89765 |==================================================================
oneDNN 3.3
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 1.84590 |=======================================================
b . 2.20049 |==================================================================
Intel Open Image Denoise 2.1
Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only
Images / Sec > Higher Is Better
a . 2.10 |====================================================================
b . 2.12 |=====================================================================
oneDNN 3.3
Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 3.29789 |==================================================================
b . 3.25571 |=================================================================
oneDNN 3.3
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 2.48058 |================================================================
b . 2.54078 |==================================================================
oneDNN 3.3
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 0.783853 |===============================================================
b . 0.811661 |=================================================================
Intel Open Image Denoise 2.1
Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only
Images / Sec > Higher Is Better
a . 4.45 |=====================================================================
b . 4.37 |====================================================================
Intel Open Image Denoise 2.1
Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only
Images / Sec > Higher Is Better
a . 4.41 |=====================================================================
b . 4.44 |=====================================================================
Embree 4.3
Binary: Pathtracer - Model: Crown
Frames Per Second > Higher Is Better
a . 109.44 |==================================================================
b . 110.46 |===================================================================
Embree 4.3
Binary: Pathtracer ISPC - Model: Crown
Frames Per Second > Higher Is Better
a . 123.36 |===================================================================
b . 123.25 |===================================================================
Embree 4.3
Binary: Pathtracer - Model: Asian Dragon
Frames Per Second > Higher Is Better
a . 126.56 |===================================================================
b . 126.98 |===================================================================
oneDNN 3.3
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 0.404445 |===============================================================
b . 0.416529 |=================================================================
oneDNN 3.3
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 0.515277 |================================================================
b . 0.520272 |=================================================================
oneDNN 3.3
Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 0.327592 |===============================================================
b . 0.339075 |=================================================================
Embree 4.3
Binary: Pathtracer ISPC - Model: Asian Dragon
Frames Per Second > Higher Is Better
a . 151.61 |===================================================================
b . 151.68 |===================================================================
easyWave r34
Input: e2Asean Grid + BengkuluSept2007 Source - Time: 240
Seconds < Lower Is Better
a . 2.980 |===============================================================
b . 3.209 |====================================================================
oneDNN 3.3
Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 0.433850 |================================================================
b . 0.437447 |=================================================================
oneDNN 3.3
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 0.223134 |=================================================================
b . 0.220395 |================================================================
oneDNN 3.3
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 0.727900 |=================================================================
b . 0.720924 |================================================================